# LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition

arXiv: https://arxiv.org/abs/2205.11420 · HTML: https://mlsanity.com/paper/2205.11420/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Knowledge Distillation Beyond Model Compression](https://mlsanity.com/paper/2007.01922/) (2020)
- [Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation](https://mlsanity.com/paper/1807.01855/) (2018)
- [BanglaWriting: A multi-purpose offline Bangla handwriting dataset](https://mlsanity.com/paper/2011.07499/) (2020)
- [End-to-End Optical Character Recognition for Bengali Handwritten Words](https://mlsanity.com/paper/2105.04020/) (2021)
- [Digging Deeper into CRNN Model in Chinese Text Images Recognition](https://mlsanity.com/paper/2011.08505/) (2020)

## Built on by (most cited first, 3 of 3)

- [Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words](https://mlsanity.com/paper/2303.07514/) (2023)
- [LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration](https://mlsanity.com/paper/2310.03669/) (2023)
- [GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer](https://mlsanity.com/paper/2509.18081/) (2025)

## Abstract (arXiv)

Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla. The complexity arises from the existence of a large number of alphabets, the presence of several diacritic forms, and the appearance of complex conjuncts. The difficulty is exacerbated by the fact that some graphemes occur infrequently but remain indispensable, so addressing the class imbalance is required for satisfactory results. This paper addresses this issue by introducing two knowledge distillation methods: Leveraging Isolated Letter Accumulations By Ordering Teacher Insights (LILA-BOTI) and Super Teacher LILA-BOTI. In both cases, a Convolutional Recurrent Neural Network (CRNN) student model is trained with the dark knowledge gained from a printed isolated character recognition teacher model. We conducted inter-dataset testing on BN-HTRd and BanglaWriting as our evaluation protocol, thus setting up a challenging problem where the results would better reflect the performance on unseen data. Our evaluations achieved up to a 3.5% increase in the F1-Macro score for the minor classes and up to 4.5% increase in our overall word recognition rate when compared with the base model (No KD) and conventional KD.